Using Motivation Assessment as a Teaching Tool for Large Undergraduate Courses: Reflections From the Teaching Team
Bibliographic record
Abstract
Introduction: Student motivation is a critical predictor of academic achievement, engagement, and success in higher education. Motivating students is a crucial aspect of effective teaching. Statement of the Problem: Although there is a wealth of research on student motivation, practical guidance for putting theory into practice in challenging teaching environments (i.e., large-format introductory courses) is lacking. We discuss a first step toward motivating students: understanding how motivated they are and using that information to inform teaching. Literature Review: Anxiety, impeded motivation, and high student-to-teacher ratio are all challenges associated with teaching foundational introductory courses, such as statistics. The Expectancy-Value-Cost model of motivation provides theoretical background to assist with these courses. We discuss the implementation and use of motivation assessments as a teaching tool. Teaching Implications: Motivation assessments are feasible and useful while teaching large-format introductory courses. Instructor reflections lend insights as to how to use these assessments to improve pedagogy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".